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bakeoff

Compare Bay Run specialists on your authorized labelled examples. Scores are provisional and cover only those examples.

Input Schema

NameRequiredDescriptionDefault
taskYes
examplesYes

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed4 schema fields changed
    • removedInput schema / additionalProperties
      Removed value: -false
    • removedInput schema / properties / examples / maxItems
      Removed value: -50
    • removedInput schema / properties / task / maxLength
      Removed value: -120
    • removedOutput schema / additionalProperties
      Removed value: -true
  2. Changed2 schema fields changed
    • removedInput schema / properties / examples / description
      Removed value: -"examples; value for this operation."
    • removedInput schema / properties / task / description
      Removed value: -"Task the model or service should perform."
  3. Changed3 schema fields changed
    • addedInput schema / properties / examples / description
      Added value: +"examples; value for this operation."
    • addedInput schema / properties / task / description
      Added value: +"Task the model or service should perform."
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
      +  "additionalProperties": true,
      +  "type": "object"
      +}
  4. Added

TDQS

B3.2/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already flag readOnlyHint=false and destructiveHint=false, so the agent knows this computes something without destroying data. The description adds real value by disclosing that scores are provisional and only cover the supplied examples, which sets expectations about the result's generalizability, but says nothing about cost, runtime, or whether the run is persisted.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two short sentences with no filler, and the scoping caveat about provisional scores is front-loaded alongside the core action. It is efficient, though bordering on under-specified rather than optimally informative.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists, so return values need not be described. However, for a two-required-parameter computation tool with zero schema description coverage, the description leaves the 'task' parameter and the example object shape entirely undefined, which an agent needs to call it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must carry the burden. It only gestures at 'examples' ('your authorized labelled examples', implying labelled ground-truth data) and says nothing at all about what 'task' means or what shape an example object should take, leaving the required nested object opaque.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (Compare) and resource (Bay Run specialists) scoped to 'your authorized labelled examples', which is more than a restatement of the name 'bakeoff'. It does not, however, explicitly contrast itself with related siblings such as recommend_specialist or get_ranking, and 'Bay Run specialists' is unexplained domain jargon.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

'On your authorized labelled examples' implies a prerequisite (authorization plus a labelled example set) and hence when the tool is applicable. But there is no explicit when-to-use/when-not guidance and no named alternative for cases where the agent cannot supply labelled examples.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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